HUG-VAS：一种基于层次NURBS的生成模型用于主动脉几何合成与可控编辑
HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing
AI总结：
HUG-VAS通过结合NURBS参数化与层次扩散模型，实现主动脉几何的精细合成与可控编辑，支持零样本生成与临床应用。
摘要：
准确的患者特异性血管几何对于诊断、规划和设备设计至关重要，但现有统计形状建模（SSM）流程依赖线性先验和拓扑特定预处理，限制了现实感、可扩展性和互操作性。我们提出了HUG-VAS，一种用于血管模型的层次NURBS生成框架，将基于NURBS的3D形状编码与扩散生成建模结合，以合成细粒度的CFD-ready主动脉解剖结构。HUG-VAS将形状分解为（i）由去噪扩散模型生成的血管中心线和（ii）由分类器自由引导扩散模型合成的横截面半径剖面，该模型基于中心线条件生成，从而解耦并保留这两个解剖层中的随机变异性。除了无条件合成外，我们通过扩散后验采样从图像衍生提示（如稀疏3D点、切片轮廓或部分表面碎片）实现训练自由、零样本条件生成，支持交互式半自动分割、编辑和在降质成像下的稳健重建。在21个患者特异性MRA案例上训练，HUG-VAS生成多分支主动脉及其超声部血管，其生物标志物分布与源群体高度匹配，其封闭的NURBS输出可直接整合到下游CFD求解器中。据我们所知，这是首个将图像衍生先验与生成形状合成通过统一的NURBS参数化、层次扩散和DPS结合的SSM框架，使从有限的临床解剖信息到模拟就绪的血管几何的实用路径成为可能。
英文摘要：
Accurate, patient-specific vascular geometry is pivotal for diagnosis, planning, and device design, yet existing statistical shape modeling (SSM) pipelines rely on linear priors and topology-specific preprocessing that limit realism, scalability, and interoperability. We present HUG-VAS, a Hierarchical NURBS Generative framework for Vascular models, that unifies NURBS-based 3D shape encoding with diffusion-based generative modeling to synthesize fine-grained, CFD-ready aortic anatomies. HUG-VAS factorizes shape into (i) vessel centerlines generated by a denoising diffusion model and (ii) cross-sectional radius profiles synthesized by a classifier-free guided diffusion model conditioned on the centerline, thereby decoupling and preserving stochastic variability across these two anatomical layers. Beyond unconditional synthesis, we enable training-free, zero-shot conditional generation via diffusion posterior sampling from image-derived prompts (e.g., sparse 3D points, slice contours, or partial surface patches), supporting interactive semi-automatic segmentation, editing and robust reconstruction under degraded imaging. Trained on 21 patient-specific MRA cases, HUG-VAS generates multi-branch aortas with supra-aortic vessels whose biomarker distributions closely match the source cohort, and whose watertight NURBS outputs directly integrate with downstream CFD solvers. To our knowledge, this is the first SSM framework that bridges image-derived priors and generative shape synthesis through a unified combination of NURBS parameterization, hierarchical diffusion, and DPS, enabling a practical path from limited clinical anatomic information to simulation-ready vascular geometry.
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